Syllabus
PROFESSIONAL ELECTIVES
Electives in Computer Vision
Unit I
Video Basics, Video Segmentation and Keyframe Extraction. Motion estimation and Compensation- Motion Segmentation – Optical Flow Segmentation- Segmentation for Layered Video Representation. Background Modeling-Shadow Detection – Object Detection -Local Features-Mean Shift: Clustering.
Unit II
Video object tracking: Template matching, Mean-shift tracking, Kalman and Particle Filters, Tracking by detection. Anamoly detection
Unit III
Data Collection and Management:Case Selection and Validity in Video Data Analysis, Collecting Custom-Made Data,Collecting Ready-Made Data,Triangulation, Data Management,Analyzing Video Data:Coding and concepts,Timing and sequence,Counts and quantifications, Rhythm and turn-taking, Studying Actors
Objectives and Outcomes
Course Objectives
- It introduces basic video analysis techniques related to segmentation, object detection and tracking.
- The course also explains how to do video data analysis in a practical manner
Course Outcomes
CO1: Understand and implement algorithms for video processing and video analysis.
CO2: Apply motion-based algorithms for identifying and tracking objects.
CO3: Understand the fundamentals of Data Analysis in Video Data.
CO4: Apply Data Analysis for Video Data through case studies.
CO-PO Mapping
| PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
| CO |
| CO1 |
2 |
2 |
2 |
1 |
2 |
|
|
|
|
|
|
|
3 |
2 |
| CO2 |
2 |
2 |
2 |
2 |
|
|
|
|
|
|
|
|
3 |
2 |
| CO3 |
3 |
2 |
1 |
3 |
2 |
|
|
|
|
|
|
2 |
3 |
2 |
| CO4 |
3 |
3 |
2 |
3 |
3 |
|
|
|
|
|
|
2 |
3 |
2 |
Evaluation Pattern
Evaluation Pattern: 70:30
| Assessment |
Internal |
End Semester |
| Mid Term Exam |
20 |
|
| Continuous Assessment Theory (*CAT) |
10 |
|
| Continuous Assessment Lab (*CAL) |
40 |
|
| **End Semester |
|
30 (50 Marks; 2 hours exam) |
*CAT – Can be Quizzes, Assignments, and Reports
*CAL – Can be Lab Assessments, Project, and Report
**End Semester can be theory examination/ lab-based examination/ project presentation
Text Books / References
Textbook(s)
Sonka M, Hlavac V, Boyle R. “Image processing, analysis, and machine vision”. 4th edition, Cengage Learning; 2015.
Richard Szeliski. “Computer Vision: Algorithms and Applications”, Springer; 2021.
Anne Nassauer, Nicolas M. Legewie, “Video Data Analysis”, SAGE Publishers, 2022.
Reference(s)
Rafeal C.Gonzalez , Richard E Wood ,”Digital Image processing”, 4th edition, person, 2018.
A.MuratTekalp. “Digital Video Processing”, Pearson;1995.
Thierry Bouwmans, FatihPorikli, Benjamin Höferlin and Antoine Vacavant, “Background Modeling and Foreground Detection for Video Surveillance: Traditional and Recent Approaches, Implementations, Benchmarking and Evaluation”, CRC Press, Taylor and Francis Group; 2014.
